Wireless Personal Communications

, Volume 109, Issue 3, pp 1925–1954 | Cite as

An Efficient Target Tracking in Directional Sensor Networks Using Adapted Unscented Kalman Filter

  • Zahra Izadi-Ghodousi
  • Mahsa Hosseinpour
  • Fatemeh Safaei
  • Amir Hossein MohajerzadehEmail author
  • Mohammad Alishahi


In this paper we have considered an efficient adapted Unscented Kalman Filter based target tracking in directional wireless sensor networks while observations are noise-corrupted. In directional sensor networks, sensors are able to observe the target only in specified (and certainly changeable) directions. Also, sensor nodes are capable of measuring the bearings (relative angle to the target). To make target tracking efficient, first, we use scheduling algorithm which determines the sensor nodes activity. Also coverage is a challenge that we will discuss in this paper as well. Sensor nodes activation algorithm directly affects the target areas coverage. Second, we use time series to predict the motion of the target. Using ARIMA, in each step of target position estimation, an area will be predicted where the target would be there with high probability. Third, we use a version of UKF, which is adjusted to the requirements of the target tracking application, to determine the position of the target with desired precision. Fourth, a routing algorithm called as C-RPL is used to perform the communications between sensor nodes in each step. Simulation results approve that the proposed efficient target tracking algorithm achieves its goals.


Directional sensor network Target tracking Unscented Kalman filter Coverage Routing ARIMA model 



This work is funded and supported by Ferdowsi University of Mashhad.


  1. 1.
    Rawat, P., Singh, K. D., Chaouchi, H., & Bonnin, J. M. (2014). Wireless sensor networks: A survey on recent developments and potential synergies. The Journal of Supercomputing,68(1), 1–48.Google Scholar
  2. 2.
    Demigha, O., Hidouci, W.-K., & Ahmed, T. (2013). On energy efficiency in collaborative target tracking in wireless sensor network: A review. Communications Surveys & Tutorials, IEEE,15(3), 1210–1222.Google Scholar
  3. 3.
    Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer networks,52(12), 2292–2330.Google Scholar
  4. 4.
    Vasuhi, S., & Vaidehi, V. (2016). Target tracking using interactive multiple model for wireless sensor network. Information Fusion,27, 41–53.Google Scholar
  5. 5.
    Lersteau, C., Rossi, A., & Sevaux, M. (2016). Robust scheduling of wireless sensor networks for target tracking under uncertainty. European Journal of Operational Research,252(2), 407–417.MathSciNetzbMATHGoogle Scholar
  6. 6.
    Mohamadi, H., Ismail, A. S., & Salleh, S. (2013). Utilizing distributed learning automata to solve the connected target coverage problem in directional sensor networks. Sensors and Actuators, A: Physical,198, 21–30.Google Scholar
  7. 7.
    Guvensan, M. A., & Yavuz, A. G. (2011). On coverage issues in directional sensor networks: A survey. Ad Hoc Networks,9(7), 1238–1255.Google Scholar
  8. 8.
    Zhu, C., Zheng, C., Shu, L., & Han, G. (2012). A survey on coverage and connectivity issues in wireless sensor networks. Journal of Network and Computer Applications,35(2), 619–632.Google Scholar
  9. 9.
    Zhao, L., Bai, G., Jiang, Y., Shen, H., & Tang, Z. (2014). Optimal deployment and scheduling with directional sensors for energy-efficient barrier coverage. International Journal of Distributed Sensor Networks,10(1), 1–9.Google Scholar
  10. 10.
    Rossi, A., Singh, A., & Sevaux, M. (2013). Lifetime maximization in wireless directional sensor network. European Journal of Operational Research,231(1), 229–241.Google Scholar
  11. 11.
    Mohamadi, H., Salleh, S., & Razali, M. N. (2014). Heuristic methods to maximize network lifetime in directional sensor networks with adjustable sensing ranges. Journal of Network and Computer Applications,46, 26–35.Google Scholar
  12. 12.
    Zhu, X., Li, J., Chen, X., & Zhou, M. (2017). Minimum cost deployment of heterogeneous directional sensor networks for differentiated target coverage. IEEE Sensors Journal,17(15), 4938–4952.Google Scholar
  13. 13.
    Khedr, A. M., & Osamy, W. (2011). Effective target tracking mechanism in a self-organizing wireless sensor network. Journal of Parallel and Distributed Computing,71(10), 1318–1326.Google Scholar
  14. 14.
    Shen, X., & Varshney, P. K. (2014). Sensor selection based on generalized information gain for target tracking in large sensor networks. IEEE Transactions on Signal Processing,62(2), 363–375.MathSciNetzbMATHGoogle Scholar
  15. 15.
    Sahoo, P. K., Sheu, J.-P., & Hsieh, K.-Y. (2013). Target tracking and boundary node selection algorithms of wireless sensor networks for internet services. Information Sciences,230, 21–38.MathSciNetGoogle Scholar
  16. 16.
    Jin, Y., Ding, Y., Hao, K., & Jin, Y. (2015). An endocrine-based intelligent distributed cooperative algorithm for target tracking in wireless sensor networks. Soft Computing,19(5), 1427–1441.Google Scholar
  17. 17.
    Pino-Povedano, S., & González-Serrano, F.-J. (2014). Comparison of optimization algorithms in the sensor selection for predictive target tracking. Ad Hoc Networks,20, 182–192.Google Scholar
  18. 18.
    Teng, J., Snoussi, H., Richard, C., & Zhou, R. (2012). Distributed variational filtering for simultaneous sensor localization and target tracking in wireless sensor networks. IEEE Transactions on Vehicular Technology,61(5), 2305–2318.Google Scholar
  19. 19.
    Luo, C., Li, W., Fan, M., Yang, H., & Fan, Q. (2014). A collaborative positioning algorithm for mobile target using multisensor data integration in enclosed environments. Computer Communications,44, 26–35.Google Scholar
  20. 20.
    Misra, S., Singh, S., Khatua, M., & Obaidat, M. S. (2015). Extracting mobility pattern from target trajectory in wireless sensor networks. International Journal of Communication Systems,28(2), 213–230.Google Scholar
  21. 21.
    Yu, Z.-J., Wei, J.-M., & Liu, H.-T. (2009). Energy-efficient collaborative target tracking algorithm using cost-reference particle filtering in wireless acoustic sensor networks. The Journal of China Universities of Posts and Telecommunications,16(1), 9–43.Google Scholar
  22. 22.
    Pino-Povedano, S., Arroyo-Valles, R., & Cid-Sueiro, J. (2014). Selective forwarding for energy-efficient target tracking in sensor networks. Signal Processing,94, 557–569.Google Scholar
  23. 23.
    Yang, W., Chen, G., Wang, X., & Shi, L. (2014). Stochastic sensor activation for distributed state estimation over a sensor network. Automatica,50(8), 2070–2076.MathSciNetzbMATHGoogle Scholar
  24. 24.
    Shang, C., Chen, G., Ji, C., & Chang, C.-Y. (2015). An efficient target tracking mechanism for guaranteeing user-defined tracking quality in WSNs. IEEE Sensors Journal,15(9), 5258–5271.Google Scholar
  25. 25.
    Feng, J., Lian, B., & Zhao, H. (2015). Coordinated and adaptive information collecting in target tracking wireless sensor networks. IEEE Sensors Journal,15(6), 3436–3445.Google Scholar
  26. 26.
    Bhuiyan, M., Wang, G., & Vasilakos, A. (2014). Local area prediction-based mobile target tracking in wireless sensor networks. IEEE Transactions on Computers,64(7), 1968–1982.MathSciNetzbMATHGoogle Scholar
  27. 27.
    Wang, X., Wang, T., Chen, S., Fan, R., Xu, Y., Wang, W., et al. (2016). Track fusion based on threshold factor classification algorithm in wireless sensor networks. International Journal of Communication Systems,30(7), e3164.Google Scholar
  28. 28.
    Nayebi-Astaneh, A., Pariz, N., & Naghibi-Sistani, M.-B. (2015). Adaptive node scheduling under accuracy constraint forwireless sensor nodes with multiple bearings-only sensing units. IEEE Transactions on Aerospace and Electronic Systems,51(2), 1547–1557.Google Scholar
  29. 29.
    Zheng, J., Bhuiyan, M. Z. A., Liang, S., Xing, X., & Wang, G. (2014). Auction-based adaptive sensor activation algorithm for target tracking in wireless sensor networks. Future Generation Computer Systems,39, 88–99.Google Scholar
  30. 30.
    Liang, Y., Feng, X., Yang, F., Jiao, L., & Pan, Q. (2013). The distributed infectious disease model and its application to collaborative sensor wakeup of wireless sensor networks. Information Sciences,223, 192–204.MathSciNetGoogle Scholar
  31. 31.
    Lin, J., Xiao, W., Lewis, F. L., & Xie, L. (2009). Energy-efficient distributed adaptive multisensor scheduling for target tracking in wireless sensor networks. IEEE Transactions on Instrumentation and Measurement,58(6), 1886–1896.Google Scholar
  32. 32.
    Mohajerzadeh, A. H., Yaghmaee, M. H., & Zahmatkesh, A. (2015). Efficient data collecting and target parameter estimation in wireless sensor networks. Journal of Network and Computer Applications,57, 142–155.Google Scholar
  33. 33.
    Hu, X., Hu, Y. H., & Xu, B. (2014). Energy-balanced scheduling for target tracking in wireless sensor networks. ACM Transactions on Sensor Networks (TOSN),11(1), 21.Google Scholar
  34. 34.
    Jiang, B., Ravindran, B., & Cho, H. (2013). Probability-based prediction and sleep scheduling for energy-efficient target tracking in sensor networks. IEEE Transactions on Mobile Computing,12(4), 735–747.Google Scholar
  35. 35.
    Shi, K., Chen, H., & Lin, Y. (2015). Probabilistic coverage based sensor scheduling for target tracking sensor networks. Information Sciences,292, 95–110.Google Scholar
  36. 36.
    Cryer, J. D., & Kellet, N. (1986). Time series analysis (Vol. 286). Berlin: Springer.Google Scholar
  37. 37.
    Djotio, T. N., & Nouho, J. S. N. (2012). Problem of standardization of configuration data in wireless sensor networks: The case of routing protocols. Wireless Sensor Network,4(7), 177.Google Scholar
  38. 38.
    Winter, T., et al. (2012). RPL: IPv6 routing protocol for low-power and lossy networks. RFC6550.Google Scholar
  39. 39.
    Vasseur, J. (2011). Terminology in low power and lossy networks. (Work in progress). Google Scholar
  40. 40.
    Gaddour, O., & Koubâa, A. (2012). RPL in a nutshell: A survey. Computer Networks,56(14), 3163–3178.Google Scholar
  41. 41.
    Agusti-Torra, A., Cervello-Pastor, C., & Fiol, M. A. (2013). ID routing mechanism for opportunistic multi-hop networks. Communications Letters, IEEE,17(12), 2388–2391.Google Scholar
  42. 42.
    Thubert, P. (2012). Objective function zero for the routing protocol for low-power and lossy networks (RPL).Google Scholar
  43. 43.
    Gnawali, O., & Levis, P. (2010). The ETX objective function for RPL.Google Scholar
  44. 44.
    Brachman, A. (2013). RPL objective function impact on LLNS topology and performance. In Internet of things, smart spaces, and next generation networking (pp. 340–351). Springer.Google Scholar
  45. 45.
    Karkazis, P., Leligou, H. C., Sarakis, L., Zahariadis, T., Trakadas, P., Velivassaki, T. H., et al. (2012). Design of primary and composite routing metrics for RPL-compliant wireless sensor networks. In 2012 international conference on telecommunications and multimedia (TEMU) (pp. 13–18). IEEE.Google Scholar
  46. 46.
    Long, N. T., Uwase, M.-P., Tiberghien, J., & Steenhaut, K. (2013). QoS-aware cross-layer mechanism for multiple instances RPL. In 2013 international conference on advanced technologies for communications (ATC) (pp. 44–49). IEEE.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer EngineeringImam Reza International UniversityMashhadIran
  2. 2.Department of Computer EngineeringFerdowsi University of MashhadMashhadIran
  3. 3.Department of StatisticsFerdowsi University of MashhadMashhadIran
  4. 4.Department of Computer EngineeringIslamic Azad University, Science and Research BranchTehranIran
  5. 5.Department of Computer Engineering, Mashhad BranchIslamic Azad UniversityMashhadIran

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